1. Technical Field
The disclosed embodiments generally relate to neurological event detector systems, devices and methods that are configured to detect the occurrence of an event or condition of interest in a patient. More particularly, the disclosed embodiments relate to systems and methods for automatically adjusting the threshold at or above which an event or condition is detected based on the difference between a target detection rate and an actual or measured detection rate associated with the event or conditions.
2. Background
Neurostimulation systems, and increasingly implantable neurostimulation systems, are being used for the treatment of various chronic diseases and neurological disorders, such as pain management, epilepsy, and movement disorders such as Parkinson's disease. Research is ongoing concerning the application of implantable neurostimulation systems for treatment of psychological disorders, headaches, and for stroke recovery and Alzheimer's disease. Other disorders that for which implantable neurostimulation systems may be applied include tic disorders, such as Tourette's disorder; mood disorders, such as major depressive disorder and bipolar disorder; and anxiety disorders, such as obsessive-compulsive disorder. Typically, a neurostimulator will be programmed to deliver stimulation to a particular nerve or region of a patient's brain on either a continuous or scheduled basis (sometimes referred to as “open-loop” stimulation) or in response to signals from the patient that are detected by the neurostimulator (sometimes referred to as “closed-loop” stimulation and “responsive” stimulation).
One such closed-loop, responsive neurostimulation system for the treatment of epilepsy has been used to deliver electrical stimulation via electrodes implanted in the brain (deep brain electrodes) and/or on the surface of the brain (cortical electrodes) in response to what the system recognizes as a neurological event (e.g., a seizure, onset of a seizure, or a precursor to a seizure). This system is described, among other places, in U.S. Pat. No. 6,016,449, issued Jan. 18, 2000 to Fischell et al., entitled “System for the Treatment of Neurological Disorders.” The disclosure of U.S. Pat. No. 6,016,449 is incorporated by reference herein in the entirety.
One or more signal processing techniques and/or algorithms typically are used in responsive systems to operate on signals being sensed by the neurostimulator from the patient in order to identify when a neurological event of interest has occurred or is occurring. Especially in cases where the neurostimulation system is wholly implantable, the robustness or accuracy or precision of these signal processing techniques usually is limited by design constraints such as limits on the amount of power the implantable device can consume before, for example, a battery has to be replaced or recharged. Typically, the signal processing techniques and associated algorithms include signal summing, squaring, subtracting, amplifying, and filtering.
Generally, the signal processing techniques and associated algorithms are used to test the signal(s) being sensed by the neurostimulator against a predetermined threshold or thresholds and, if a particular threshold is exceeded, the neurostimulator system will register the detection of an event or condition. In many neurostimulation systems, the detection of an event triggers delivery of a particular therapy (e.g., delivery of a stimulation pulse of certain amplitude, pulse width, frequency and waveform shape; delivery of a volume of a drug; or delivery some other stimulus, such as a sensory stimulus (auditory, visual, etc.); or some combination of stimuli.) Optimally, the thresholds identified will maximize the likelihood that the neurological event or condition of interest will be detected, and minimize the likelihood of “false positives,” that is, conditions under which a threshold is exceeded but the event or condition of interest is not, in fact, occurring. U.S. Pat. No. 6,459,936, issued Oct. 1, 2002 to Fischell et al. for “Methods for Responsively Treating Neurological Disorders” describes some of these signal processing techniques and threshold-setting objectives. The disclosure of U.S. Pat. No. 6,459,936 is incorporated by reference herein in the entirety.
Some examples of particular signal processing techniques and/or algorithms include a half wave detector, a line length analysis, and an area function analysis, each of which is described at a high level herein and more fully in, for example, U.S. Pat. No. 6,473,639, issued Oct. 29, 2002 to Fischell et al., entitled “Neurological Event Detection Procedure Using Processed Display Channel Based Algorithms and Devices Incorporating These Procedures,” U.S. Pat. No. 6,480,743, issued Nov. 12, 2002 to Kirkpatrick et al., entitled “System and Method for Adaptive Brain Stimulation,” and U.S. Pat. No. 6,810,285, issued Oct. 26, 2004 to Pless et al. for “Seizure Sensing and Detection Using an Implantable Device.” The disclosures of U.S. Pat. Nos. 6,473,639, 6,480,743, and 6,810,285 are each incorporated by reference herein in the entirety.
A half wave detector measures the occurrence of what are predefined to constitute half waves in an impinging electrocorticographic signal, or more generally, an electroencephalogic (EEG) signal from a patient within a specified half wave time window. (That is, in order to constitute or qualify as a “half wave” the impinging signal may have to have a certain amplitude or frequency, and the slope of the waveform may have to change to a predetermined degree.) The number of half waves occurring in one window is compared to a threshold value for a number of half waves. If the number of detected half waves exceeds the threshold, then detection of an event (e.g., onset of an epileptic seizure) is registered and certain therapy may be triggered (or at least further processing of signals from the patient might be accomplished in order to decide whether to deliver therapy). The half wave detector can be likened to a band pass filter insofar as it will identify an event or condition as detected based on such parameters as minimum and maximum frequencies and/or amplitudes.
A “line length” analysis can be undertaken by (1) accumulating the sample-to-sample amplitude variation in an EEG signal within a predefined time window (or normalizing the line lengths per unit time) (i.e., adding up all the line lengths that represent how much variation the signal in the samples is undergoing); (2) accumulating the sample-to-sample amplitude variation in the signal within the next window (of the same predefined, duration or normalized to the same time unit); and (3) comparing the total line lengths of the first window to the second window. If the sum of the line lengths in the subsequent window is 200% greater than the sum of the line lengths in the first window, then this might suggest that an event or condition of interest has been detected, since the signal measured in the second window would seem to be varying a lot more than the signal in the first window. The percent difference between the accumulated line lengths therefore can be used as a threshold parameter, because when the percent difference is increased, it will tend to decrease the sensitivity of detection and lower the detection rate, and when the percent difference is decreased, it will tend to increase the sensitivity of detection and thus increase the detection rate.
An “area function” analysis can be accomplished by calculating the area under the curve of a signal incident on a window having a predetermined length of time and then comparing it to the area under the curve for the same incident signal in a next window of time having the same predetermined duration. Alternatively, the samples used in calculating the area under the curve can be normalized per unit time, so that samples taken in different length windows can be meaningfully compared. An incident signal that is hovering around zero on the y axis will have a small total area as compared to an incident signal that is more active, e.g., one that is oscillating between the most positive and the most negative possible values on the y axis. Thus, if the change in area for a signal from one window to a subsequent window is large, this may suggest that the event or condition of interest is occurring. The area differences from window to window could be compared to a percent difference threshold, for example, a threshold of 250%, such that if the total area in a subsequent window is 250% greater than it was in a previous window, the threshold is met.
Ideally, the threshold above which an event or condition should be detected should be adjustable depending on variables such as the patient's physiological condition at different times. For example, where a closed-loop neurostimulation system is being used to treat epilepsy, the signals representative of the occurrence of seizures (or of the onset or precursors of seizures) may be quite different depending upon such things as the time of day, the time of the month, whether the patient is awake or sleeping, etc. It would be desirable to vary the value of a given threshold at those different times to optimize detection of the events of interest. As a practical matter though, most signal processing techniques that might be used to reset the threshold values based on time of day or changing physiological states of the individual patient would be complex and therefore would consume considerable computational power, which would make them less desirable, especially when the detection signal processing is implemented in an implantable device. The threshold values therefore are usually fixed relative to time of day or changing physiological conditions of a patient at least for the time between the patient's visits to the clinician. That is, the threshold values can be adjusted by the clinician during an office visit to either increase or decrease the sensitivity of the detector, but then they will remain fixed until the patient comes in for his or her next visit.
However, the ideal threshold for a given parameter used for detection may vary with a particular physiological condition, such as hormonal changes that might occur over a female patient's menstrual cycle. Further examples of when the optimal value for a given threshold might vary over time because the EEG signals measured at those time are quite different include between sleep and wake cycles, when medications are at different levels of concentration in a patient's system, and when a patient is sleep deprived or under unusual stress.
In addition, and where detection of an event or condition is associated with delivery of a therapy or treatment (e.g., electrical stimulation), the clinical efficacy of the detection/stimulation combination may depend on delivery of a minimum amount of stimulation to the central nervous system, just as the efficacy of some drug therapies depend on a minimum dose of the drug per day. Detection thresholds that are too low may result in a system that does not stimulate enough, or quickly enough, to alter the activity of the central nervous system in the desired manner (e.g., to control seizures).
Similarly, and again when detection is used to trigger electrical stimulation therapy, too sensitive a detector may result in more frequent stimulation than the central nervous system can accommodate, and the central nervous system therefore may not be as effectively modulated as when less frequent stimulation is delivered. In addition, a neurostimulator that detects and stimulates in response to the detections will have time constants associated with the feedback, sensing and stimulation subsystems. Thus, setting a detection threshold that is too low may result in a detection rate that exceeds the dynamic capabilities of the system. If this occurs, not all pathologic activity may be appropriately detected and stimulated or stimulation may be delivered too late to, for example, have the desired effect on epileptiform activity.
Further, asking the clinician to identify threshold values for parameters that will affect the detection rate can result in some frustration, as not all clinicians may have the depth of technical understanding about how the neurological event detector operates in order to choose optimum values. For example, for a responsive neurostimulator, a clinician likely would more appreciate a system that would allow him or her to specify a minimum “dose” of stimulation per day, which would correspond to a particular detection rate, and then have the system automatically choose the parameters to use as thresholds and to set the values for those parameters that will achieve that particular detection rate.